Algorithms (Introduction)
 Readings: [SG] Ch. 1, Ch. 2
 Chapter Outline:
1.
2.
3.
4.
5.
Chapter Goals
What are Algorithms [SG] Ch. 2.1
Pseudo-Code to Express Algorithms [SG] Ch. 2.2
Some Simple Algorithms
Examples of Algorithmic Problem Solving
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 1
1. Goals of Algorithm Study
To develop framework for instructing
computer to perform tasks (solve problems)
Algorithm as a
“means of specifying how to solve a problem”
To introduce the idea of decomposing
complex tasks into simpler tasks;
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(UIT2201: 2a. Algorithms) Page 2
Algorithms to solve problems
 Computing devices are dumb
 How to instruct a dumb mechanical /
computing device to solve a problem
 Express instructions using
“a small, basic set of primitive instructions”
Example: Working with a pet dog
1.
2.
Primitive oral instructions:
“sit”, “heel”, “fetch”, “roll”…
Primitive visual instructions:
sign language
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 3
Dog obedience training…
Source: http://lacetoleather.com/obedience.html
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(UIT2201: 2a. Algorithms) Page 4
 Chapter Outline:
1. Chapter Goals
2. What are Algorithms [SG] Ch. 2.1
1.
2.
3.
4.
Real Life Examples (origami, recipes)
Simple Example: Calculating Mile-per-Gallon
Definition of Algorithm
A = B + C (self-study, [SG]-C1.2, 2.1)
3. Pseudo-Code to Express Algorithms
4. Some Simple Algorithms
5. Examples of Algorithmic Problem Solving
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(UIT2201: 2a. Algorithms) Page 5
2. Computer Science and Algorithms…
Computer Science is…
the study of algorithms, including
 their formal and mathematical properties
 Their hardware,
 Their linguistic (software) realisations
 Their applications (to diverse areas)
(Read carefully Ch-1.2 of [SG])
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(UIT2201: 2a. Algorithms) Page 6
Algorithm
al go rithm [SG3]: A well-ordered collection of
unambiguous and effectively computable operations
that, when executed, produces a result and
halts in a finite amount of time.
 Unambiguous,
 Effectively computable operation,
 produces a result,
 Halts in a finite amount of time.
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(UIT2201: 2a. Algorithms) Page 7
Algorithms: Real Life Examples
 Many Real-Life Analogies
 Directions: How to go to Changi Airport
 Origami: The Art of Paper Folding
 Cooking: Recipe for preparing a dish
Keep in Mind:
1. Framework: “Input, output, hardware, software ”
2. Algorithm: “The actual step-by-step instructions”
3. Abstraction: “Decomposing / Simplifying”
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A Recipe Analogy for Algorithm
Recipe for a chocolate mousse
Ingredients:
8 ounces of semi-sweet chocolate pieces,
2 tablespoon of water,
1/4 cup of powdered suger,
6 separated eggs,
…
...
“Melt chocolate and 2 tablespoons water in double boiler.
When melted, stir in powdered sugar; add butter bit by bit. Set aside.
Beat egg yolks until thick and lemon-colored, about 5 minutes.
Gently fold in chocolate. Reheat slightly to melt chocolate, if
necessary. Stir in rum and vanilla.
Beat egg white until foamy. Beat in 2 tablespoons sugar; beat until
stiff peaks form. Gently fold egg whites into chocolate-yolk mixture.
Pour into individual serving dishes.
Chill at least 4 hours.
Serve with whipped cream, if desired. Makes 6 to 8 servings.”
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(UIT2201: 2a. Algorithms) Page 9
Framework (for the Recipe Example)
Sample Problem: Making chocolate mousse
Algorithms
Framework for Cooking
Input
Hardware
Software
Output
Ingredients
Utensils, oven, pots, pens, chef
Recipe
Chocolate mousse
Ingredients
Primitive (Basic) Operations:
(software)
(hardware)
recipe
Utensils,
oven, baker
• pour, mix, stir, drip, stir-fry
• bake, boil, melt,
• open-oven-door, remove-pan
• measure time, volume, weight
Chocolate mousse
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(UIT2201: 2a. Algorithms) Page 10
Multiple Levels of Abstraction (1)
 The level of abstraction
(level of detail of the instructions) should vary with the
sophistication of the hardware / software tools
Hierarchy of abstraction levels…
L0: Prepare Chocolate mousse for 5 people
L1: Prepare chocolate mixture;
Prepare chocolate-yoke mixture;
Prepare egg-white batter;
…
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(UIT2201: 2a. Algorithms) Page 11
Multiple Levels of Abstraction (2)
 The level of abstraction
(level of detail of the instructions) should vary with the
sophistication of the hardware / software tools
Hierarchy of abstraction levels…
L0: Prepare Chocolate mousse for 5 people
L1: Prepare chocolate mixture;
Prepare chocolate-yoke mixture;
L2: Melt
chocolate
2 tablespoons
water …
Prepare
egg and
white
batter;
…
…stir in powdered sugar; add butter bit-by-bit
L3: …take a little powdered sugar,
pour it into the melted chocolate, stir it in,
take a little more sugar, pour…, stir…,
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(UIT2201: 2a. Algorithms) Page 12
Multiple Levels of Abstraction (3)
Hierarchy of abstraction levels…
L0: Prepare Chocolate mousse for 5 people
L1: Prepare chocolate mixture;
Prepare chocolate-yoke mixture;
L2: Melt
chocolate
2 tablespoons
water …
Prepare
egg and
white
batter;
…
…stir in powdered sugar; add butter bit-by-bit
L3: …take a little powdered sugar,
pour it into the melted chocolate, stir it in,
take a little more sugar, pour…, stir…,
L4: …take 2365 grains of powdered sugar, pour them into the
melted chocolate, pick up a spoon and
use circular motion to stir it in, …
L5: …move your arm towards the ingredients at an angle of 14º,
at a velocity of 0.5m per second, …
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(UIT2201: 2a. Algorithms) Page 13
Multiple Levels of Abstraction (4)
Recall Recurring Principle:
Multiple Levels of Abstraction
 Why have some many levels of abstraction?






L0: Good for “bosses”
L1: Good for experienced chefs
L2: Good for inexperienced chefs
L3: Good for newbie chefs
L4: Good for an “automated” process
L5: Good for people who needs to
program the automated process
 Question: What is the appropriate level?
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(UIT2201: 2a. Algorithms) Page 14
Summary: Cooking Analogy
Framework:
“Cooking or Recipe mini-language”
Algorithm:
“Recipe for Chocolate Mousse”
(step-by-step instructions)
Problem Decomposition
 L0 task is decomposed into L1 tasks
Prepare the Chocolate Mixture;
Prepare Chocolate-Yoke Mixture;
Prepare Egg-White Batter;
 Each L1 task is further decomposed into L2 tasks
 And so on…
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(UIT2201: 2a. Algorithms) Page 15
An Origami Analogy for Algorithm
Framework:
“Origami or Paper-Folding language”
Algorithm:
“Sequence of Paper-Folding Instructions”
(step-by-step instructions for each fold)
Problem Decomposition
 Start with a Bird Base;
 Finish the Head;
 Finish the Legs; Finish the Tail;
http://www.origami-instructions.com/origami-bird-base.html
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 16
Primitives in Origami
Primitive folds in Origami
 Valley fold, Mountain fold, Triangle fold
 Squash fold, Petal fold
 Inside and Outside Reverse folds
Patterns of Often-used Folds:





Kite base  Diamond base,
Square base  Bird base,
Blintz base, Boat base, Helmet base,
Organ base, Pig base,
Water bomb base (Balloon base)
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 17
Problem Decompostion in Origami
Making an Origami Crane:
http://www.origami-instructions.com/origami-crane.html
Problem Decomposition





Start with a Bird Base;
Fold the outside corners inside… …
Using reverse fold, make neck and tail
Finish the Head;
Finish the Wings;
Modular Origami
http://www.origami-instructions.com/modular-origami-instructions.html
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(UIT2201: 2a. Algorithms) Page 18
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 19
Example of
Simple Algorithm
for Problem Solving
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(UIT2201: 2a. Algorithms) Page 20
Simple Example: Computing miles-per-gallon
Problem:
 Given: Starting mileage, ending mileage,
amount of gas used for a trip;
 Calculate average “miles per gallon” for the trip
An Instance of the Problem:
StartMiles = 12345; EndMiles = 12745; GasUsed = 20 (gallons)
The Calculations:
Distance = (12745 – 12345) = 400 (miles);
Average = 400/20 = 20 (miles/gallon)
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 21
Simple Miles-per-gallon Algorithm:
Call this “GasUsed”
Call this “StartMiles”
Call this “EndMiles”
Call this “Average”
Call this “Distance”
Figure 2.3
Algorithm for Computing Average Miles per Gallon
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(UIT2201: 2a. Algorithms) Page 22
Simple Miles-per-gallon Algorithm:
Problem:
 Given: Starting mileage, ending mileage,
amount of gas used for a trip;
 Calculate average “miles per gallon” for the trip
A More Concise Version:
ALGORITHM Avg-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
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(UIT2201: 2a. Algorithms) Page 23
Tracing the “State of the Algorithm”
ALGORITHM Avg-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
Input to Algorithm:
30, 2201, 2861
GasUsed
???
StartMiles
???
EndMiles
???
Distance
???
Average
???
Output of Algorithm:
Algorithm
Step ???.
CPU
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
???
Our abstract
model of
the computer
(UIT2201: 2a. Algorithms) Page 24
Tracing the “State of the Algorithm”
ALGORITHM Avg-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
Input to Algorithm:
30, 2201, 2861
GasUsed
30
StartMiles
2201
EndMiles
2861
Distance
???
Average
???
Output of Algorithm:
Algorithm
Step 1.
CPU
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
???
Our abstract
model of
the computer
(UIT2201: 2a. Algorithms) Page 25
Tracing the “State of the Algorithm”
ALGORITHM Ave-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
Input to Algorithm:
30, 2201, 2861
GasUsed
30
StartMiles
2201
EndMiles
2861
Distance
660
Average
???
Output of Algorithm:
Algorithm
Step 2.
(2861 – 2201)
= 660
CPU
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
???
Our abstract
model of
the computer
(UIT2201: 2a. Algorithms) Page 26
Tracing the “State of the Algorithm”
ALGORITHM Avg-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
Input to Algorithm:
30, 2201, 2861
GasUsed
30
StartMiles
2201
EndMiles
2861
Distance
660
Average
22
Output of Algorithm:
Algorithm
Step 3.
(660 / 30)
= 22
CPU
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
???
Our abstract
model of
the computer
(UIT2201: 2a. Algorithms) Page 27
Tracing the “State of the Algorithm”
ALGORITHM Avg-MPG
1. Get values for GasUsed, StartMiles, EndMiles;
2. Let Distance be (EndMiles – StartMiles);
3. Let Average be Distance / GasUsed;
4. Print the value of Average
5. Stop
Input to Algorithm:
30, 2201, 2861
GasUsed
30
StartMiles
2201
EndMiles
2861
Distance
660
Average
22
Output of Algorithm:
Algorithm
Step 4.
CPU
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
22
Our abstract
model of
the computer
(UIT2201: 2a. Algorithms) Page 28
Scratch version…
Go check out the
Scratch version
yourself
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(UIT2201: 2a. Algorithms) Page 29
Example: Adding two (m-digit) numbers
Input:
 Two positive m-digit decimal numbers (A and B)
A = ( am-1, am-2, …., a0 )
B = ( bm-1, bm-2, …., b0 )
Output:
 The sum C = A + B
C = ( cm, cm-1, cm-2, …., c0 )
An instance of the Problem:
a= 5982
b= 7665
c=13647
m=4
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Self Study:
Read [SG] Ch 1.2, 2.1
Make sure you
understand how the
algorithm work;
(UIT2201: 2a. Algorithms) Page 30
How to “derive” the algorithm
Adding is something we all know
 done it a thousand times, know it “by heart”
How do we give the algorithm?
 A step-by-step instruction
 to a dumb machine
Try an example:
3492
8157
“Imagine you looking at yourself solving it”
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(UIT2201: 2a. Algorithms) Page 31
Algorithm: Finding sum of A & B
Addition Algorithm for C = A + B
Step 1: Set the value of carry to 0
Step 2: Set the value of i to 0.
Skip this for now
Cover during tutorials.
Step 3: While the value of i is less than or equal to (m – 1), repeat steps 4 through 6
Step 4: Add ai and bi to the current value of carry, to get x
Step 5: If x < 10 then
Let ci = x, and reset carry to 0.
else (* namely, in this case x  10 *)
Let ci = x – 10 and reset carry to 1.
Step 6: Increase the value of i by 1.
Step 7: Set cm to the value of carry.
Step 8: Print the final answer cm, cm-1, …., c0
Step 9: Stop.
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
Self Study:
Read [SG] Ch 1.2, 2.1
Make sure you
understand how this
algorithm work;
(UIT2201: 2a. Algorithms) Page 32
 Chapter Outline:
1. Chapter Goals
2. What are Algorithms
3. Pseudo-Code to Express Algorithms [SG]-Ch 2.2




Communicating algorithm to computer
Pseudo-Code for expressing Algorithms
Model of a Computer, Variables and Arrays
Primitive Operations and examples
4. Some Simple Algorithms
5. Examples of Algorithmic Problem Solving
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(UIT2201: 2a. Algorithms) Page 33
Expressing Algorithms: Issues
Problems/Difficulties with using English
 Imprecise instructions; ambiguity
 Job can often be done even if instructions are not
followed precisely
 Modifications may be done by the person following
the instructions;
 NOT suitable for instructing a Computer
 Computer needs to told PRECISELY what to do;
Instructions must be PRECISE;
Cannot be vague or ambiguous
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(UIT2201: 2a. Algorithms) Page 34
3. Expressing Algorithms for a Computer
To communicate algorithm to computer
 Need way to “represent” the algorithm
 Cannot use English
Can use computer language
 machine language and
 programming languages (Java, Pascal, C)
But, these are too tedious (&technical)
Use Pseudo-Code and Scratch instead…
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(UIT2201: 2a. Algorithms) Page 35
Pseudo-Code to express Algorithms
Pseudo-Code
 Mixture of computer language and English
 Somewhere in between
 precise enough to describe what is meant
without being too tedious
 Examples:
 Let c be 0;
 c  0;
 Sort the list of numbers in increasing order;
Need to know both syntax and semantics
 syntax – representation
 semantics – meaning
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(UIT2201: 2a. Algorithms) Page 36
Definition of Algorithm:
An algorithm for solving a problem
“a finite sequence of unambiguous, executable steps
or instructions, which, if followed would ultimately
terminate and give the solution of the problem”.
 Note the keywords:
 Finite sequence of steps;
 Unambiguous;
 Executable;
 Terminates;
(Read more in [SG]-Ch 1)
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(UIT2201: 2a. Algorithms) Page 37
Are these Algorithm?
Problem 1: What is the largest integer
INPUT: All the integers { … -2, -1, 0, 1, 2, … }
OUTPUT: The largest integer
Algorithm:
 Arrange all the integers in a list in decreasing order;
 MAX = first number in the list;
 Print out MAX;
 WHY is the above NOT an Algorithm?
(Hint: How many integers are there?)
Problem 2: Who is the tallest women in the world?
 Algorithm: To be discuss during Tutorial
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(UIT2201: 2a. Algorithms) Page 38
Our Current Model of a Computer
CPU
Major Components of a Computer
(from Figure 5.2 of [SG])
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(UIT2201: 2a. Algorithms) Page 39
Our Current Model of a Computer
Memory: Large Number of “Storage Boxes”:
 Each memory (or storage box) can store information;
 Can give name to these memory boxes (variable names)
 Can only store one number at a time
(old value are overwritten, and gone!)
 CPU (Central Processing Unit):
 Can read data from memory (variables) into CPU
 Can do complex calculations (+, - , *, /, etc) in CPU
 Can store answers back to memory (variables)
Input / Output Devices:
 Monitor, Keyboard, Mouse, Speakers, Microphone, etc
 Can read data from Input Devices into the CPU,
 Can print data from CPU to Output Devices
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(UIT2201: 2a. Algorithms) Page 40
Memory Model: Variables
 Variables (or Storage Boxes)
 Computers work with data (numbers, words, etc)
 Data must be stored (in storage boxes)
Each storage box can store one number at any time
30
 Each storage box is given a name, called a variable
Examples: Distance, Average, j
Distance
660
Operations of a Variable (storage box)
 Read: read the content of (value stored in) the box
 Write: store a new value into the box
IMPT: When a new value is written to a variable,
the old value is lost forever.
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(UIT2201: 2a. Algorithms) Page 41
Arrays (contiguous storage boxes)
Often deal with many numbers (of same type)
 Eg: Quiz score for all students in the class
 One storage box for each score (need 25 boxes)
 Have 25 different variables
 QuizScore1, QuizScore2, … , QuizScore25
30
 Give them a common variable name, say, A




Such as A1, A2, A3, … , A25
Store as an “array” A[1], A[2], … , A[100]
They are stored in contiguous storage boxes
One box for each A[k]
we treat each of them as a variable,
each is assigned a storage “box”
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 42
Primitive Operations
 To “tell” a computer what to do, we need




“a basic set of instructions”
That is understood and executable by computer
Here, we call them “primitive operations”
Most primitive operations are very low level
 Will express algorithms using these primitive
operations
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(UIT2201: 2a. Algorithms) Page 43
Primitive Operations of a Computer
 Three types of primitive operations:
1. Sequential operation:
 assignment statement, read/print statements
2. Conditional operation:
 if statement
 case statement
3. Looping (iterative) operation:
 while loop,
 for loop,
 Operations/statements are executed
sequentially (from top to bottom), one-by-one
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(UIT2201: 2a. Algorithms) Page 44
Type 1: Simple Operations/Statements
Assignment statements (examples)





Set Count to 5;
Assign X the value of (C+B)/2;
Let Interest be Rate*Principle*Duration;
Let A[3] be 8;
Let Smallest be A[i+3];
Another (more concise) way to express these…





Count  5;
X  (C+B)/2;
Interest  Rate*Principle*Duration;
A[3]  8;
Smallest  A[i+3];
Semantics:
1. Compute value of expression on the
RHS of the assignment statement.
2. Store the compute value as the new
value of the variable on LHS.
(Note: Old value of variable is lost.)
Note: These statements are executed one-by-one
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 45
Execution of some Sequential Statements
Try it out yourself!
Count 143
Count  5;
X  (C+B)/2;
A[3]  8;
Smallest  A[i+3];
B
10
C
30
X 205
A[1]
20
A[2]
15
A[3]
??
CPU
Assume this is the initial
state of the computation…
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 46
Tracing (or exercising) an algorithm…
Sample Algorithm
1. J  3;
2. X  14;
3. J  X + 2*J;
J
?
3
3
20
X
?
?
14
14
Given an algorithm (above left), to exercise it means
 to “trace” the algorithm step-by-step; and
 observe the value of each variable after each step;
 Good to organize as a “table” as shown above (right)
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 47
More Simple Operations/Statements
 Some Input / Output Statements;




Get the value of N;
Read in the value of A[1], A[2], A[3], A[4];
Print the string “Welcome to my Intelligent Agent”;
Print “Your IQ is”, A, “ but your EQ is”, A/3;
Another way of expressing them…




Read ( N );
Read ( A[1], A[2], A[3], A[4] );
Print “Welcome to my Intelligent Agent”;
Print “Your IQ is”, A, “ but your EQ is”, A/3;
Note: These statements are executed one-by-one
(Can assume each takes constant unit of time to execute)
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 48
Miles-per-gallon (revisited)
To obtain a better report, use more print statements;
 Print out details in nice report format;
ALGORITHM
1. Read ( StartMiles, EndMiles, GasUsed );
2. Distance  (EndMiles – StartMiles);
3. Average  Distance / GasUsed;
4. Print “Trip Report”
5. Print “ Your StartMiles =“, StartMiles;
6. Print “ Your EndMiles
=“, EndMiles;
7. Print “ Gas Used
=“, GasUsed;
8. Print “ Average km/litre=“, Average;
9. Print “End of Trip Report”;
5. Stop
…
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 49
More Example: To swap two variables
Given two values stored in A and B;
Wanted: An algorithm to exchange the values stored;
Example:
 Input:
A = 15;
 Required Output: A = 24;
B = 24;
B = 15;
Two Incorrect Algorithms
ALG 1:
1. A  B;
2. B  A;
A
15
B
24
ALG 2:
A
15
B
24
1. B  A;
2. A  B;
Error: One of the values was over-written;
HW: What is a correct algorithm to swap A & B?
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 50
Type 2: Conditional Statements
if statement
 to take different actions based on condition
Syntax
if (condition)
then (Step A)
else (Step B)
endif
true
condition?
Step A
false
Step B
if (condition)
then (Step A)
endif
Semantics
Either Step A or Step B
is executed, but never both.
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 51
Example 1 of Conditional Statement (1)
Syntax
if (Average >= 25)
then Print “Good..”;
else Print “Bad..”;
true
endif
Semantics
Print “Good..”
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
Average >= 25
false
Print “Bad..”
(UIT2201: 2a. Algorithms) Page 52
Example 1 of Conditional Statement (2)
Miles-per-Gallon Problem (revisited)
 Suppose we consider good petrol consumption to be Average
that is >= 25.0 miles / gallon
 Determine if petrol consumption for trip is Good!
Example:
 Average = 15.0, then “Not good petrol consumption”
 Average = 30.6, then “Good petrol consumption”
ALGORITHM
1. ... (* Steps to compute Average ... *)
2. if (Average >= 25)
3.
then Print “Good Petrol Consumption”;
4.
else Print “Not good petrol comsumption”;
5. endif
6. Stop
…
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 53
Version 2 of Miles-per-Gallon Algorithm
Average mile per
gallon version 2
Figure 2.4
Second Version of the Average Miles per Gallon Algorithm
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 54
Version 2 of Miles-per-Gallon Algorithm
 Combine the two parts into one longer algorithm
 With printout on good /bad petrol consumption
ALGORITHM Miles-per-Gallon; version 2)
1. Read ( StartMiles, EndMiles, GasUsed );
2. Distance  (EndMiles – StartMiles);
3. Average  Distance / GasUsed;
4. Print “Average Mileage is”, Average;
5. if (Average >= 25)
6.
then Print “Good Petrol Consumption”;
7.
else Print “Not good petrol comsumption”;
8. endif
9. Stop
…
Homework:
Modify the Scratch program to incorporate this.
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 55
Example 2 of Conditional Statement
Alg. to read in a mark and print out if student pass.
 Let’s say that the passing mark is 40;
Examples:
 mark = 25; Expected Output is “Student fail”
 mark = 45; Expected Output is “Student pass”
 mark = 99; Expected Output is “Student pass”
Solution: Use an if-then-else statement
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 56
Example 2 of Conditional Statement
The Algorithm:
Algorithm:
1. Read (mark); (*get value of mark*)
2. if (mark < 40)
3. then (print “Student fail”)
4. else (print “Student pass”)
5. endif
…
Try executing algorithm with some cases:
 When mark = 30; Output is “Student fail”
 When mark = 42; Output is “Student pass”
 When mark = 95; Output is “Student pass”
Note: in the above,
 either 3 or 4 is executed; never both
Q: What about the different grades of passes?
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 57
Two if Statements (one after another)…
Suppose grade “D” is defined as 40-49 marks
1.
2.
3.
4.
5.
6.
7.
Read (mark); (* Get value of mark *)
if (mark < 40)
then (print “Student fail”)
endif;
if (mark >= 40) and (mark < 50)
then (print “Grade D”)
endif;
…
Try some cases:
 When mark = 30; Output is “Student fail”
 When mark = 42; Output is “Grade D”
 When mark = 95; What is output?
Where is the “error”?
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 58
“Nested” if Statements (one inside another)…
1. Read (mark); (* Get value of mark *)
2. if (mark < 40)
3.
then (print “Student fail”)
4.
else if (mark < 50)
5.
then (print “Grade D”)
6.
else (print “Grade C or better”)
7.
endif
7. endif;
…
Try some cases:
 When mark = 30; Output is “Student fail”
 When mark = 42; Output is “Grade D”
 When mark = 95; Output is “Grade C or better”
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 59
A Complicated if Statement
read in mark (*from the terminal*)
if (mark < 40) then (Grade  “F”)
else if (mark < 50) then (Grade 
else if (mark < 60) then (Grade 
else if (mark < 70) then (Grade 
else if (mark < 80) then (Grade 
else (Grade  “A+”)
endif
print “Student grade is”, Grade
“D”)
“C”)
“B”)
“A”)
endif
endif
endif
endif
This is a complicated if statement;
 Study it carefully to make sure you understand it;
 Can you come up with this algorithm yourself?
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 60
Type 3: Iterative (looping) operations
Recall Recurring Principle:
The Power of Iterations
Iterative statement:
 Tells computer to do “something” multiple times
 Tells computer to “loop” multiple times
 Loop condition: (also called terminating condition)
a condition (true/false) to tell when to stop looping!
 Pre- and Post-loop iterative statements
 Pre-test: Test loop condition before looping
 Post-test: Test loop condition after looping
 Question: What if the loop condition is never satisfied?
 Infinite loop!
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 61
Type 3: Iterative (looping) operations
 Recall the underlying principles:
“The power of iterations”
 Iterative statement:
 Tells computer to do “something” multiple times
 Tells computer to “loop” multiple times
 Loop condition: (also called terminating condition)
a condition (true/false) to tell when to stop looping!
 Pre- and Post-loop iterative statements
 Pre-test: Test loop condition before looping
 Post-test: Test loop condition after looping
 Question: What if the loop condition is never satisfied?
 Infinite loop!
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 62
Iterative operation: while-loop
 the while-loop
 loop multiple times
while condition is true
 Syntax
condition?
true
while (condition) do
(some sequence
of statements)
endwhile
 Semantics…
Some sequence
of statements;
Execute this group of statements
repeatedly as long the condition is true.
Exits only if condition is false.
If condition is never false, infinite loop!
© Leong Hon Wai, 2003--
LeongHW, SoC, NUS
false
(UIT2201: 2a. Algorithms) Page 63
Exercising a while-loop
j  1;
while (j <= 3) do
print j;
j  j + 1;
endwhile
print “--- Done ---”
Output:
1
2
3
--- Done ---
(* General Loop *)
Read(n);
j  1;
while (j <= n) do
print j;
j  j + 1;
endwhile
print “--- Done ---”
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 64
Danger with using a while-loop
j  1;
while (j <= 3) do
print j;
j  j + 1;
endwhile
print “--- Done ---”
j  1;
while (j >= 0) do
print j;
j  j + 1;
endwhile
print “--- Done ---”
Output:
1
2
3
--- Done ---
Output:
1
2
Infinite loop!
3
4
5
...
To
Toerr
errisishuman,
human,
ToToforgive,
divine!
really foul
things up,
you need a computer.
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 65
Miles-per-Gallon (with while loop)
Average mile per
gallon version 2
Figure 2.5
Third Version of the Average Miles per Gallon Algorithm
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 66
Conditional and Iterative Operations
Pretest loop
 Loop condition tested at the beginning of each pass
through the loop
 It is possible for the loop body to never be executed
 While loop
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 67
Algorithms Problem Solving
 Readings: [SG] Ch. 2
 Chapter Outline:
1.
2.
3.
4.
Chapter Goals
What are Algorithms
Pseudo-Code to Express Algorithms
Some Simple Algorithms (Continued in next ppt file)
1. Computing Sum
2. Structure of Basic Iterative Algorithm
5. Examples of Algorithmic Problem Solving
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 68
Thank you!
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 69
Additional Slides…
 The next few slides are for your info only.
 They are on the for-loop
 a special iterative statement
 for loops will not be tested in UIT2201.
 If you don’t know it, and don’t want to,
 You can do perfectly fine with the while-loop.
 But if you already know it, you can use it
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 70
Looping Primitive – for-loop
 First, the for-loop
 loop a “fixed” or
(pre-determined)
number of times
 Syntax
j  a;
(j <= b)?
false
true
for j  a to b do
(some sequence
Some sequence
of statements;
of statements)
endfor
j  j+1;
 Semantics…
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 71
“Exercising the alg”: for and while
for j  1 to 4 do
print 2*j;
endfor
print “--- Done ---”
Output:
2
4
6
8
--- Done ---
j  1;
while (j <= 4) do
print 2*j;
j  j + 1;
endwhile
print “--- Done ---”
Output:
2
4
6
8
--- Done ---
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 72
Recall: Algorithm for summing A & B
Addition Algorithm for C = A + B
Step 1: Set the value of carry to 0
Step 2: Set the value of i to 0.
Step 3: While the value of i is less than or equal to (m – 1), repeat steps 4 through 6
Step 4: Add ai and bi to the current value of carry, to get x
Step 5: If x < 10 then
Let ci = x, and reset carry to 0.
else (* namely, in this case x  10 *)
Let ci = x – 10 and reset carry to 1.
Step 6: Increase the value of i by 1.
Step 7: Set cm to the value of carry.
Step 8: Print the final answer cm, cm-1, …., c0
Step 9: Stop.
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 73
Algorithm: A = B + C (in pseudo-code)
Can re-write the C=A+B algorithm concisely as follows:
Alg. to Compute C = A + B;
(* sum two m-bit integers *)
1. carry  0;
2. i  0;
3. while (i < m) do
4.
x  a[i] + b[i] + carry;
5.
if (x < 10)
6.
then { c[i]  x; carry  0; }
7.
else { c[i]  x - 10; carry  1; }
8.
endif
9.
i  i + 1;
10. endwhile;
11. c[m]  carry;
12. print c[m], c[m-1], …., c[0]
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 74
Algorithm: A = B + C (in pseudo-code)
Can re-write the C=A+B algorithm concisely as follows:
Alg. to Compute C = A + B:
(*sum two big numbers*)
carry  0;
for i  0 to (m-1) do
x  a[i] + b[i] + carry ;
if (x < 10)
then ( c[i]  x; carry  0; )
else ( c[i]  x – 10; carry  1; )
endif
endfor;
c[m]  carry;
Print c[m], c[m-1], …., c[0]
© Leong Hon Wai, 2003-LeongHW, SoC, NUS
(UIT2201: 2a. Algorithms) Page 75